The longterm objective of this work is to advance breast cancer prevention and risk prediction by identifying molecular events underlying early initiation/predisposition to breast cancer. Current prevention therapies for breast cancer are proven to be effective at reducing estrogen receptor positive (ER+) cancers but have no impact on preventing ER negative (ER-) cancers. This differential effect on breast cancer prevention provides strong evidence that ER+ and ER- cancers have different pathways to carcinogenesis, but the earliest steps in tumorigenesis remain poorly understood. Furthermore, there are no existing methods to accurately predict an individual woman's risk for either ER+ or ER- breast cancer. Investigation into early drivers of breast cancer through study of premalignant breast tissues will yield information on biomarkers for risk prediction and relevant pathways that may inform new prevention strategies. In this research proposal, high throughput molecular studies of benign breast biopsy tissues will be performed in a large cohort of women who underwent benign breast biopsy, with information available on later breast cancers and ER status of the tumors. A case control subset will be developed from the cohort, with equal numbers of ER+, ER-, and control subjects. In Aim 1 we will perform gene expression profiling and create gene signatures associated with risk of ER+ and ER- breast cancer. In Aim 2, benign breast biopsy tissues will be evaluated for select candidate driver mutations (the most common based on published findings) and their associations with risk of ER+ and ER- breast cancer. A subset of samples with paired breast tissue and germline DNA will undergo whole exome sequencing to investigate somatic mutations associated with cancer. Gene pathways identified in profiling studies will be correlated with driver mutations. In Aim 3, information on gene expression profiles and somatic mutations in benign breast disease tissues will be used to improve risk stratification for ER+ and ER- breast cancers. This work will result in the creation of gene signatures predictive of ER+ and ER- BC, which will likely improve risk prediction for ER+ BC and increase uptake of ER+ prevention therapies. Genomic signatures of ER- BC risk will help to identify key oncogenic pathways and new prevention treatments for ER- BC.